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AI automation

AI process automation

AI agents take over the repetitive, high-volume work so your people spend their time on the decisions that actually need them.

  • AI agents
  • RAG
  • LLM integrations
  • Workflow automation
  • Human-in-the-loop
  • EU AI Act

This is for established companies where a handful of processes quietly consume a disproportionate share of your team's time — reading documents, triaging emails, entering orders, answering the same customer questions — and where the people doing that work are too senior to be spending their days on it.

The goal is not to automate for its own sake. Most of the value comes from choosing the right process first: one that is high-volume, repetitive and tolerant of a review step, where a measurable amount of time is being spent every week. We start by finding that process and estimating what automating it is actually worth, before anyone writes a line of code.

What we build are AI agents grounded in your own data and wired into the systems you already run, with a human kept in the loop wherever a decision carries consequences. Every action leaves an audit trail, and the whole thing is built to comply with GDPR and the EU AI Act from the start, not bolted on afterwards.

How we deliver it

Find the right process

We spend about two weeks mapping where your team's time actually goes — which tasks are high-volume, repetitive, rule-shaped and tolerant of a review step. Not everything that looks automatable is worth automating, and some of it should not be touched with AI at all.

For the candidates that survive, we estimate the return in plain terms: hours spent now, realistic accuracy, the cost to build and run, and what human review will still cost. You get a short, costed shortlist and an honest recommendation on what to automate first — and what to leave alone.

OutputA ranked shortlist with a per-process ROI estimate and a recommendation

Prove it in a pilot

Before committing to production, we build a bounded pilot on the single process the ROI case ranks highest. It runs on real data and real cases, alongside your existing process rather than replacing it, so you can compare the agent's output against how the work is done today.

The pilot exists to answer one question honestly: is the accuracy high enough, and the value real enough, to justify building the full thing? Sometimes the answer is no, and it is far cheaper to learn that in a pilot than in production.

OutputA working pilot with measured accuracy and a go / no-go decision

Build with guardrails

Once the pilot proves out, we build for production in two-week iterations, each ending in something you can inspect. The agent is grounded in your own documents and data — retrieval over your knowledge, not a generic chatbot guessing — so its answers trace back to sources you control.

Guardrails are part of the design, not an afterthought. Consequential actions wait for human approval, the agent acts only above a confidence threshold and falls back gracefully when it is unsure, and every step it takes is logged. The point is an agent you can trust to know the limits of what it knows.

OutputProduction agents with human-in-the-loop controls and full logging

Integrate and comply

An agent is only useful when it can act where your work already happens, so we connect it directly to your ERP, CRM and internal systems through their APIs — reading and writing the same records your team does, under permissions you set.

Compliance is designed in alongside the integration. We keep your data private, with EU data residency and on-premise options where the sensitivity calls for it, document how the system makes decisions for the EU AI Act, and settle the GDPR questions — lawful basis, retention, data subject rights — before go-live rather than after.

OutputLive integrations plus GDPR and EU AI Act documentation

Launch and measure

We roll the agent into production gradually, usually starting with a share of real volume while people watch the results, and widen it as the numbers hold. Before that, load testing, a security pass and failure drills confirm it behaves under pressure and degrades safely when a model or an upstream system is unavailable.

From day one in production we measure the three things that matter: accuracy against human judgement, the quality of what it produces, and the running cost per case. Those numbers are visible to you, not buried — so the business case stays something you can check rather than take on faith.

OutputA monitored rollout with live accuracy, quality and cost metrics

Operate and improve

AI systems drift — your documents change, your data shifts, model providers update underneath you — so the work does not end at launch. We watch the metrics, retune prompts and retrieval against real cases, and tighten or relax the guardrails as the evidence tells us where review is still earning its keep.

Everything transfers to you: the agents, the prompts and configuration, and the monitoring. You own what we built and can run, inspect and change it without us. We stay on only where you want us to, on terms you control.

OutputFull ownership — agents, prompts, configuration and monitoring

What you get

  • A process audit and a per-process ROI estimate before any build
  • AI agents integrated into your ERP, CRM and internal systems
  • An internal knowledge base — an AI assistant grounded in your own documents
  • Human-in-the-loop controls, confidence thresholds and graceful fallback
  • An audit trail on every action the agent takes
  • GDPR and EU AI Act compliance documentation, with EU and on-premise data options
  • Monitoring of accuracy, quality and running cost, and full ownership at handover

How we think about AI automation

The hard part of this work is not the model — it is knowing where AI belongs and where it does not. A large language model is the right tool for messy, language-shaped tasks that tolerate review: reading a document, drafting a reply, classifying a request. For anything that must be exactly right every time, deterministic rules are cheaper, faster and safer, and we will tell you when a few lines of logic beat an agent. Reaching for AI where a rule would do is how automation projects earn their bad reputation.

We also treat accuracy as a number, not a feeling. An agent that is right 95% of the time is only safe if the remaining 5% is caught, which is why the design always pairs a confidence threshold with a human review path and a clear fallback. The agent is built to defer when it is unsure rather than to guess confidently — the failure we design against is a wrong answer delivered as if it were certain.

And we keep your data yours. Grounding the agent in your own content through retrieval means it answers from sources you control instead of inventing, and EU data residency and on-premise options mean sensitive information need never leave infrastructure you trust. Private, auditable and compliant is the default here, not a premium add-on.

Common questions

How do you decide what to automate first?

We start from where your team's time actually goes, then filter for processes that are high-volume, repetitive and tolerant of a review step — the ones where automation pays back quickly and a mistake can be caught. Each candidate gets a plain ROI estimate covering the time spent today, realistic accuracy, and the cost to build and run. You get a ranked shortlist and a recommendation; automating for its own sake is how these projects lose money, so we are deliberate about sequencing.

Will the AI make things up?

That is the risk we design against hardest. The agent is grounded in your own documents and data through retrieval, so its answers come from sources you control rather than from the model's general memory. On top of that it acts only above a confidence threshold, hands consequential decisions to a person, and falls back gracefully when it is unsure — the aim is an agent that knows the limits of what it knows and defers instead of guessing.

How do you handle GDPR and the EU AI Act?

Both are designed in from the start rather than bolted on before go-live. We keep your data private, with EU data residency and on-premise deployment options where the sensitivity calls for it, and we document how the system reaches its decisions so it stands up to EU AI Act scrutiny. The GDPR questions — lawful basis, retention, data subject rights and who can see what — are settled during the build, and every action the agent takes is logged for audit.

What if AI turns out to be the wrong tool?

Then we say so, ideally in the pilot before you have spent real money. Plenty of processes are better served by deterministic rules — cheaper to run, faster, and exactly right every time — and we would rather recommend a few lines of logic than sell you an agent that fits badly. The bounded pilot exists precisely so a no-go decision is cheap, and a no is a legitimate and common outcome.

Who owns and runs the system afterwards?

You do. At handover the agents, the prompts and configuration, and the monitoring all transfer to your team, with the documentation and training to run and change them without us. Because the same senior people who scoped the work built it, the handover is a transfer of something they understand fully, not a translation through a chain of account managers. We stay involved only where you want us to, on terms you set.

Have a process worth automating?

Start with a short, fixed-fee discovery: we map where your team's time goes, find the best candidate and estimate the ROI before anyone builds anything.

Book a discovery call